Your Claude agent has access to a tool that retrieves customer records. A teammate has noticed that the agent occasionally calls the tool with arguments the schema does not declare, and the tool's downstream service returns an error each time. The teammate proposes loosening the schema so the tool accepts whatever arguments the model produces.How would you respond?
Answer(s): B
The supplied question identifies B as the intended answer. Tool schemas are contracts between Claude and executable application code. When the downstream service accepts only a defined set of arguments, relaxing that schema merely shifts invalid data farther into the system and increases runtime failures.Anthropic's current tooling provides an even stronger implementation of this principle through strict tool use. Setting strict: true constrains tool inputs to the declared JSON Schema, preventing undeclared properties, missing required values, and incompatible parameter types where the supported schema subset is used. Anthropic explicitly recommends strict tool use for validated parameters, type-safe function calls, and reliable agentic workflows.In a non-strict or legacy implementation, the application should still validate arguments before dispatch and convert invalid calls into structured tool errors that Claude can interpret and potentially correct. A prompt instruction can reinforce behavior, but it should not replace deterministic validation. C and D weaken the system boundary and knowingly send invalid calls downstream.Therefore, maintain the contract rather than adapting the contract to malformed model output.Relevant Claude Developer topics: Agent Construction, tool schemas, strict tool use, JSON Schema, parameter validation, structured errors, retry behavior, and defensive execution boundaries.
A new Claude model release includes performance improvements for several reasoning tasks but has changed the format of its responses to system prompts that use multi-section instructions. Your application uses multi-section system prompts heavily. Initial evaluation on the application's actual workload shows the new model performs 8 percent better on reasoning tasks but produces malformed output on roughly 3 percent of requests because of the format change. The team is debating whether to upgrade.How would you decide?
The supplied examination page marks B. The scenario already demonstrates why model upgrades must be treated as evaluated software changes rather than automatic replacements: the new model improves one metric while introducing a regression in another.Anthropic's official model-selection guidance recommends creating benchmark tests specific to the application's use case, testing models with the application's actual prompts and data, comparing response quality and edge-case performance, and weighing performance against operational tradeoffs. Therefore, the correct action is to adapt the multi-section system prompt to the new model's behavior and repeat the evaluation. Only after the formatting regression is eliminated---or reduced below an explicitly acceptable threshold---should the upgrade proceed.A incorrectly assumes that an 8% reasoning improvement numerically compensates for a 3% malformed-output rate; these metrics measure different consequences and cannot simply be subtracted. C treats the known incompatibility only downstream instead of first correcting the prompt/model interaction. D permanently rejects future improvement and is inconsistent with controlled lifecycle evolution.The engineering principle is migration through regression testing and adaptation, not blind upgrading or permanent version avoidance.Relevant Claude Developer topics: Systems Life Cycle, model migration, regression evaluation, prompt adaptation, compatibility testing, deployment gates, and continuous evolution.
You are setting up a Claude application that will run a mix of multi-turn conversations and one-off requests.You want to use caching techniques to reduce token costs where they apply. A teammate suggests caching the model's output as well, so the application does not have to make duplicate Claude calls when similar queries arrive.You would apply prompt caching to...
Answer(s): D
The supplied exam source marks D. Claude prompt caching is designed for repeated prompt prefixes, not semantic caching of generated answers. High-value cache candidates include stable system prompts, long instructions, tool definitions, shared background documents, repeated examples, and the previously accumulated prefix of a multi-turn conversation.Anthropic explains that prompt caching reuses a matching prompt prefix and can substantially reduce processing time and input-token cost on subsequent requests. The cache operates across the request structure---tools, system content, and messages up to the relevant cache boundary. It is particularly useful for prompts with many examples, large repeated context, repetitive instructions, and long multi-turn conversations.B describes response caching, which is a separate application-level technique and is not what Anthropic's prompt caching feature does. C targets the portion that usually changes most, making it a poor general cache boundary. A is incorrect because multi-turn workloads are a major prompt-caching use case.Therefore, maximize reusable stable prefixes and place changing request-specific content after them where practical.Relevant Claude Developer topics: Claude API Mechanics, prompt caching, cache prefixes, token-cost optimization, static context, system prompts, multi-turn conversations, and API efficiency.
You are designing an agent that handles a multi-step research task. You want the agent to break the task into smaller pieces, hand each piece to a focused subagent, and consolidate the results.The agent pattern you would apply is...
Answer(s): A
The supplied Claude Developer source explicitly marks A. The scenario contains the defining elements of an orchestrator/subagent architecture: decomposition of a larger objective, delegation of independent subtasks to specialized workers, and aggregation of their outputs by a coordinating agent.This architecture is appropriate when subtasks can be performed with focused context or specialized tools. Instead of forcing one agent to carry every intermediate detail, the orchestrator can formulate assignments, launch suitable subagents, receive condensed results, identify missing information, and synthesize the final research product. This also enables context isolation and potentially parallel execution.Anthropic's published multi-agent architecture uses this orchestrator-worker approach for research workloads: a lead agent decomposes a query, delegates work to specialized subagents, and then integrates their findings. This is particularly useful where exploration is broad and individual subtasks benefit from independent context windows. The broader model-selection guidance also identifies orchestrator strategies as useful where work can be partitioned among worker models.B describes storage rather than delegation. C is a context-management technique, not a decomposition pattern. D centralizes all responsibilities in a single loop.Relevant Claude Developer topics: Agent Patterns, orchestration, subagents, task decomposition, specialization, delegation, context isolation, and result consolidation.
A teammate has asked you to explain why your Claude agent's tools include detailed descriptions in the tool definition, even when the tool name is already descriptive. The teammate suggests removing the descriptions to simplify the tool definitions.How would you respond?
Answer(s): C
The supplied exam source identifies C. Tool descriptions are part of Claude's runtime decision context. A descriptive function name can indicate what a tool appears to do, but it may not clearly tell Claude when to use it, when not to use it, what assumptions apply, or how it differs from similar tools.Anthropic's official tool-use documentation states that Claude determines whether to call a tool based on the user's request and the tool's description. Its tool-definition guidance defines description as detailed plaintext describing what the tool does, when it should be used, and how it behaves. Anthropic's troubleshooting guidance also identifies ambiguous descriptions as a cause of selecting the wrong tool and recommends differentiating tools by when they should be used, not only by what they do.A can supplement descriptions with examples but should not universally replace them. B incorrectly assumes names provide sufficient semantics. D removes the information from Claude's actual decision context.Therefore, tool descriptions are operational metadata for model routing, not merely documentation for developers.Relevant Claude Developer topics: Agent Construction, tool definitions, tool descriptions, tool selection, schema design, disambiguation, routing, and reliable tool use.
Post your Comments and Discuss Anthropic CCDV-F exam prep with other Community members:
Our website is free, but we have to fight against AI bots and content theft. We're sorry for the inconvenience caused by these security measures. You can access the rest of the CCDV-F content, but please register or login to continue.